Quantization Parameter Values Research Articles

Versatile Video Coding-Post Processing Feature Fusion: A Post-Processing Convolutional Neural Network with Progressive Feature Fusion for Efficient Video Enhancement

Advanced video codecs such as High Efficiency Video Coding/H.265 (HEVC) and Versatile Video Coding/H.266 (VVC) are vital for streaming high-quality online video content, as they compress and transmit data efficiently. However, these codecs can occasionally degrade video quality by adding undesirable artifacts such as blockiness, blurriness, and ringing, which can detract from the viewer’s experience. To ensure a seamless and engaging video experience, it is essential to remove these artifacts, which improves viewer comfort and engagement. In this paper, we propose a deep feature fusion based convolutional neural network (CNN) architecture (VVC-PPFF) for post-processing approach to further enhance the performance of VVC. The proposed network, VVC-PPFF, harnesses the power of CNNs to enhance decoded frames, significantly improving the coding efficiency of the state-of-the-art VVC video coding standard. By combining deep features from early and later convolution layers, the network learns to extract both low-level and high-level features, resulting in more generalized outputs that adapt to different quantization parameter (QP) values. The proposed VVC-PPFF network achieves outstanding performance, with Bjøntegaard Delta Rate (BD-Rate) improvements of 5.81% and 6.98% for luma components in random access (RA) and low-delay (LD) configurations, respectively, while also boosting peak signal-to-noise ratio (PSNR).

Learning-based CU partition prediction for fast panoramic video intra coding

In recent years, Virtual Reality (VR) technology has been applied in various aspects of life. As an indispensable component of VR, panoramic video offers viewers immersive experiences, thereby attracting widespread attention. The high resolution of panoramic video poses significant challenges for its storage and transmission, requiring efficient video compression. The Versatile Video Coding (VVC) exhibits superior coding efficiency, albeit at the expense of a several dozen-fold increase in encoding complexity. In this paper, we propose a fast Coding Unit (CU) partition algorithm for intra-coding based on Support Vector Machine (SVM), which selects appropriate features for online training of multiple classifiers based on frame texture characteristics. Additionally, in response to distortion issues in panoramic video coding, we divide video frames into different regions and adjust the Quantization Parameter (QP) values of CUs within each region to improve the encoding efficiency. Experimental results show that the proposed method achieves an average encoding time reduction of 46.21%, with an average BDBR increase of only 1.01%, achieving a good trade-off between time-saving and encoding efficiency.

Optimized quantization parameter selection for video-based point cloud compression

Point clouds are sets of points used to visualize three-dimensional (3D) objects. Point clouds can be static or dynamic. Each point is characterized by its 3D geometry coordinates and attributes such as color. High-quality visualizations often require millions of points, resulting in large storage and transmission costs, especially for dynamic point clouds. To address this problem, the moving picture experts group has recently developed a compression standard for dynamic point clouds called video-based point cloud compression (V-PCC). The standard generates two-dimensional videos from the geometry and color information of the point cloud sequence. Each video is then compressed with a video coder, which converts each frame into frequency coefficients and quantizes them using a quantization parameter (QP). Traditionally, the QPs are severely constrained. For example, in the low-delay configuration of the V-PCC reference software, the quantization parameter values of all the frames in a group of pictures are set to be equal. We show that the rate-distortion performance can be improved by relaxing this constraint and treating the QP selection problem as a multi-variable constrained combinatorial optimization problem, where the variables are the QPs. To solve the optimization problem, we propose a variant of the differential evolution (DE) algorithm. Differential evolution is an evolutionary algorithm that has been successfully applied to various optimization problems. In DE, an initial population of randomly generated candidate solutions is iteratively improved. At each iteration, mutants are generated from the population. Crossover between a mutant and a parent produces offspring. If the performance of the offspring is better than that of the parent, the offspring replaces the parent. While DE was initially introduced for continuous unconstrained optimization problems, we adapt it for our constrained combinatorial optimization problem. Also, unlike standard DE, we apply individual mutation to each variable. Furthermore, we use a variable crossover rate to balance exploration and exploitation. Experimental results for the low-delay configuration of the V-PCC reference software show that our method can reduce the average bitrate by up to 43% compared to a method that uses the same QP values for all frames and selects them according to an interior point method.

Research on Quantization Parameter Decision Scheme for High Efficiency Video Coding

High-Efficiency Video Coding (HEVC) is one of the most widely studied coding standards. It still uses the block-based hybrid coding framework of Advanced Video Coding (AVC), and compared to AVC, it can double the compression ratio while maintaining the same quality of reconstructed video. The quantization module is an important module in video coding. In the process of quantization, quantization parameter is an important factor in determining the bitrate in video coding, especially in the case of limited channel bandwidth. It is particularly important to select a reasonable quantization parameter to make the bitrate as close as possible to the target bitrate. Aiming at the problem of unreasonable selection of quantization parameters in codecs, this paper proposes using a differential evolution algorithm to assign quantization parameter values to the coding tree unit (CTU) in each frame of 360-degree panoramic video based on HEVC so as to strike a balance between bitrate and distortion. Firstly, the number of CTU rows in a 360-degree panoramic video frame is considered as the dimension of the optimization problem. Then, a trial vector is obtained by randomly selecting the vectors in the population for mutation and crossover. In the mutation step, the algorithm generates a new parameter vector by adding the weighted difference between two population vectors to a third vector. And the elements in the new parameter vector are selected according to the crossover rate. Finally, the trial vector is regarded as the quantization parameter of each CTU in the CTU row to encode, and the vector with the least rate distortion is selected. The algorithm will produce the optimal quantization parameter combination for the current video. The experimental results show that compared to the benchmark algorithm of HEVC reference software HM-16.20, the proposed algorithm can provide a bit saving of 1.86%, while the peak signal-to-noise ratio (PSNR) can be improved by 0.07 dB.

Smart algorithm in wireless networks for video streaming based on adaptive quantization

SummaryNowadays, smart multimedia network services have become crucial in the healthcare system. The network parameters of Quality of Service (QoS) are widely affecting the efficiency and accuracy of multimedia streaming in wireless environments. This paper proposes an adaptation framework model, which makes a relation between the QP (quantization parameter) in H.264 and H.265 codecs and the QoS of 5G wireless technology. Besides, the effect of QP and packet loss have been studied because of their impact on video streaming. Packet loss of 5G wireless network characteristic is emulated to determine the impact of QP on the received video quality using objective and subjective quality metrics such as PSNR (peak signal to noise ratio), SSIM (structure similarity), and DMOS (differential mean opinion score). In this research, a Testbed is implemented to stream the encoded video from the server to the end users. The application model framework has automatically evaluated the QoE (Quality of Experience). Accordingly, the model detects the defect of network packet loss and selects the optimum QP value to enhance the QoE by the end‐users. The application has been tested on low and high video motions with full high definition (HD) resolution (1920 × 1080) which were taken from ( https://www.xiph.org/downloads/). Test results based on the objective and subjective quality measurements indicate that an optimal QP = 35 and QP = 30 have been chosen for low and high motion respectively to satisfy user QoE requirements.

Integral sliding mode fault tolerant control for unmanned surface vessels with quantization: Less iterations

This paper introduces an integral sliding mode quantized control strategy for unmanned surface vessels (USVs) with thruster faults and signal quantization over networks. The main challenge focuses on the co-design of dynamic quantization parameter adjustment strategy (DQPAS) and fault tolerance ability. The existing literature has solved it with a heavy computation burden. To reduce the repeated iteration of quantization parameter, an improved DQPAS with a well-chosen initial value of quantization parameter is designed. Based on it, an integral sliding mode fault tolerant controller with fault estimation is proposed to suppress the oscillation amplitudes of yaw angle and yaw velocity from the very beginning. Finally, comparison simulation results have demonstrated that the derived strategy is effective.

Content-based coding tree unit level rate-quantization model for intra-coding in high efficiency video coding standard using convolutional neural network

In almost all video applications, a video rate control algorithm (RCA) is used by the encoder. The RCA tunes the quantization parameter (QP) to match the encoded bit rate to the available capacity of the communication channel or storage media. Conventional RCAs usually utilize a rate-quantization (R-Q) or a rate-distortion (R-D) model for rate control. A content-based R-Q model for intra coding tree units (CTUs) of the high-efficiency video coding standard is proposed. The model is a convolutional neural network that observes pixels of a CTU and its intraprediction reference pixels and it estimates required bit counts for intracoding the CTU for all QP values simultaneously. The proposed model can be easily used by any video RCA. A given RCA just selects a proper QP for which the estimated bit counts are closer to the allocated bit budget. The evaluation results show a high accuracy for the model. According to simulation results, the mean absolute normalized bit error at CTU level is 19.66% and it decreases to 6.85% at the frame level. Compared with similar networks, the proposed structure has a very low computational complexity.

Visual saliency guided perceptual adaptive quantization based on HEVC intra-coding for planetary images.

Due to the limited storage space of spacecraft and downlink bandwidth in the data delivery during planetary exploration, an efficient way for image compression onboard is essential to reduce the volume of acquired data. Applicable for planetary images, this study proposes a perceptual adaptive quantization technique based on Convolutional Neural Network (CNN) and High Efficiency Video Coding (HEVC). This technique is used for bitrate reduction while maintaining the subjective visual quality. The proposed algorithm adaptively determines the Coding Tree Unit (CTU) level Quantization Parameter (QP) values in HEVC intra-coding using the high-level features extracted by CNN. A modified model based on the residual network is exploited to extract the saliency map for a given image automatically. Furthermore, based on the saliency map, a CTU level QP adjustment technique combining global saliency contrast and local saliency perception is exploited to realize a flexible and adaptive bit allocation. Several quantitative performance metrics that efficiently correlate with human perception are used for evaluating image quality. The experimental results reveal that the proposed algorithm achieves better visual quality along with a maximum of 7.17% reduction in the bitrate as compared to the standard HEVC coding.

Video Compression for Screen Recorded Sequences Following Eye Movements

With the advent of smartphones and tablets, video traffic on the Internet has increased enormously. With this in mind, in 2013 the High Efficiency Video Coding (HEVC) standard was released with the aim of reducing the bit rate (at the same quality) by 50% with respect to its predecessor. However, new contents with greater resolutions and requirements appear every day, making it necessary to further reduce the bit rate. Perceptual video coding has recently been recognized as a promising approach to achieving high-performance video compression and eye tracking data can be used to create and verify these models. In this paper, we present a new algorithm for the bit rate reduction of screen recorded sequences based on the visual perception of videos. An eye tracking system is used during the recording to locate the fixation point of the viewer. Then, the area around that point is encoded with the base quantization parameter (QP) value, which increases when moving away from it. The results show that up to 31.3% of the bit rate may be saved when compared with the original HEVC-encoded sequence, without a significant impact on the perceived quality.

Perceptual VVC quantization refinement with ensemble learning

Compressing videos while maintaining an acceptable level of Quality of Experience (QoE) is indispensable. To this aim, a feasible method is to further increase the Quantization Parameter (QP) of video stream to eliminate visual redundancy, simultaneously utilizing perceptual characteristics of Human Visual System (HVS) to impose a threshold constraint on the maximum QP. In this paper, we employ Just Noticeable Distortion (JND) to characterize the aforementioned threshold constraint, thereby avoiding perceptual loss during QP refinement process. We propose an effective JND-based algorithm for QP optimization, in which a video saliency detection is introduced to extract regions of interest, a refinement model based on a lightweight network is designed to predict QP value and an ensemble learning method to improve generalization performance. Theoretical analysis and experimental results demonstrate that the proposed algorithm has been successfully applied to Versatile Video Coding (VVC) to achieve significant bitrate reduction without sacrificing perceived quality.

Denoising and Inpainting for Point Clouds Compressed by V-PCC

With the development of immersive video, the quality of compressed 3D content has become an important issue. Video-based Point Cloud Compression (V-PCC) is a popular compression method for point cloud sequences; it achieves the highest quality among MPEG proposals. Compressed point clouds suffer from various artifacts when a high quantization parameter (QP) is used. Examining the causes and types of V-PCC artifacts that occur, we propose a framework to remove the highly noticeable outlier and crack artifacts caused by V-PCC so as to improve compressed point cloud visual quality. A subjective experiment showed that our approach provides significantly improved visual quality, and the improvement becomes more obvious with increasing QP values. Objective evaluation with point-to-point Mean Squared Error (p2p-MSE) shows our proposed method can improve point cloud quality and provides competitive results with lower complexity compared with other methods for point cloud outlier removal and inpainting.

Rate Control for Predictive Transform Screen Content Video Coding Based on RANSAC

In predictive transform video coding, optimal bit allocation and quantization parameter (QP) estimation are important to control the bit rate of blocks, frames and the whole sequence. Common solutions to this problem rely on trained models to approximate the rate-distortion (R-D) characteristics of the video content during coding. Moreover, these solutions are mainly targeted for natural content sequences, whose characteristics differ greatly from those of screen content (SC) sequences. In this article, we depart from such trained R-D models and propose a low-complexity RC method for SC sequences that leverages the availability of information about the R-D characteristics of previously coded blocks within a frame. Namely, our method first allocates bits at the frame- and block-levels based on their motion and texture characteristics. It then approximates the R-D and R-QP curves of each block by a set control points and random sample consensus (RANSAC). Finally, it computes the appropriate block-level QP values to attain a target bit rate with the minimum distortion possible. The proposed RC method is embedded into a standard High-Efficiency Video Coding (H.265/HEVC) encoder and evaluated on several SC sequences. Our results show that our method not only attains better R-D performance than that of H.265/HEVC and other methods designed for SC sequences but also attains a more constant and higher reconstruction quality on all frames.

HEVC quantization parameter selection algorithm based on inter-frame dependency

Massive inter predictive modes are adopted in latest High Efficiency Video Coding (HEVC) standard to eliminate temporal redundancies, which results in stronger inter-frame dependency among neighboring frames than previous standards like H.264. The inter-frame dependency makes currently independent rate-distortion optimization (RDO) non-optimal any more. Quantization parameter (QP) selection algorithm taking inter-frame dependency into consideration is supposed to optimize RDO based rate control greatly. According to our research, the inter-frame dependency is reflected by the linear relationship between QP change (ΔQP) and the resulting change of distortion (ΔD). An adaptive QP selection algorithm for global RDO is proposed based on the modeling function between ΔD and ΔQP in this paper. Firstly, based on intensive statistic analysis, three parameters (initial QP ($\overline {QP}$), the length of pictures of group (GOP), and the average of SATD of one frame) are used to formulate the relationship between ΔD and ΔQP. Secondly, the resulting rate change ΔR relative to ΔQP is also formulated similarly. Thirdly, optimized Lagrangian multiplier (λ) is calculated with these two mathematic models. Finally, we refine QP values based on the optimized λ in terms of dependent RDO. The experimental results show that the proposed frame-level QP selection algorithm can decrease Bj⊘tegaard Delta BitRate (BD-BR) by about 1.62% at the random-access (RA) configuration and 1.13% at the low-delay (LD) configuration, respectively. At the same time, it doesn’t increase complexity significantly.

Fast in-frame coding algorithm for HEVC based on probability of mode selection

Subject of Research. The in-frame encoding process in the standard of high-efficiency encoding of video images is considered. The analysis of mode selection probability is carried out; the algorithm of fast prediction mode selection is proposed. Method. It is proposed to select a set of 11 modes with the highest probability of selection of the 35 possible modes of the standard “default” algorithm, as input data for further refined selection. Depending on the results obtained, another stage is added, where the choice is reduced up to 4 modes. The selected modes are used in a two-stepoptimization process, which selects the best mode for the current encoding block. Main Results. The proposed algorithm is implemented in the C++ development environment. A number of standard video sequences with four quantization parameter values is used for testing. The results of the carried out experiments show that the proposed fast algorithm reduces the encoding time by 17.03 % compared to HM-16.20 reference software, with an increase in transmission speed by 1.26 % and a loss of quality only by 0.033 dB. Practical Relevance. The proposed fast algorithm provides a reduction in computational costs with an extremely negligible loss of quality of the restored video sequence. It can be used as a standard H265 video codec block for replacement of the existing in-frame prediction block in the reference program to speed up the video compression process.

A perceptual rate control algorithm based on luminance adaptation for HEVC encoders

This paper proposes a rate control algorithm by selecting a proper quantization parameter (QP) based on perceptual luminance adaptation in a single-loop encoding fashion. In this paper, the proposed algorithm uses the visual characteristics of humans to adaptively decide the number of bits available for a pixel. Then, a base QP is selected based on the proposed R–λ model. The proposed rate control determines the range of QP based on the base QP and calculates the maximum and minimum bpps within that range. The optimal bpp is obtained from the bpp range by considering the visual characteristics of the human being, and the QP value is determined by the optical bpp through the proposed R–λ model. In the proposed rate control algorithm, the QP value is selected based on the R–λ model by considering the perceptual luminance adaptation model at the CTU level. The number of target bits is decided to decide the QP, subject to visual sensitivity based on JND thresholds. With the use of the proposed rate control algorithm, bits for non-noticeable regions can be saved, and the remaining bits can be consumed for perceptually noticeable regions to enhance the overall subjective quality with the similar amount of total bits. The proposed method shows lower average variances of bits and PSNR fluctuations. Also, the proposed method achieves an approximately 0.19 higher MOS value on average under DSCQS test, compared with the conventional R–λ model-based rate control algorithm.

Optimal CTU-Level Rate Control Model for HEVC Based on Deep Convolutional Features

This paper proposes an optimal rate control model based on deep neural network (DNN) features to improve the coding tree unit (CTU)-level rate control in high-efficiency video coding for conversational videos. The proposed algorithm extracts high-level features from the original and previously reconstructed CTU blocks based on a predefined DNN model of the visual geometry group (VGG-16) network. Then, the correlation of the high-level feature and quantization parameter (QP) values of previously coded CTUs is explored for subjective visual characteristics to estimate the CTU-level rate control model parameters (alpha and beta) and the bit allocation of each CTU. Therefore, this paper also proposes a new model for Lambda estimation for each CTU by improving its relationship with the estimated bits per pixel to control the rate and relative distortion. Furthermore, the Lambda and QP boundary settings were adjusted based on the proposed perceptual model to ensure the rate control accuracy of each CTU. The results of experiments with the proposed algorithm, when compared to the rate control model in HM-16.20, reveal higher bitrate accuracy and an average BD-rate gain based on PSNR, SSIM, and MSSSIM metrics using the low-delay-P configuration.

Video Coding Optimization for Virtual Reality 360-Degree Source

To provide excellent visual experience for customers, virtual reality (VR) sources require higher resolutions and better visual quality than traditional picture sequences. The content of a VR video can be mapped into a sphere by playing devices to present a 360° scene, which is usually called VR360 in industrial community. The most popular formats for VR360 sources are the equirectangular projection (ERP) and the cubemap projection (CMP). Both ERP and CMP pictures can be effectively projected to a virtual three-dimensional spherical surface for rendering. It brings a new challenge to the compression of VR video sources, which is how to reallocate proper bit-rate to match mainstream projection formats. The most intuitive way to deal with this challenge is to empirically assign a fixed quantization parameter (QP) to each coding unit according to its position, which evidently lacks precision, rationality, and thus, degrades coding performance. This research proposes a new entropy equilibrium optimization (EEO) methodology to enhance the coding performance of VR360 videos. Specifically, we develop a spherical bit-rate equalization strategy to obtain a block-level Lagrangian multiplier (lambda, λ) for the rate-distortion optimization process in video coding. The appropriate QP value for each block is then dynamically determined in accordance with its λ. Based on our EEO methodology, we develop two algorithms, EEOA-ERP and EEOA-CMP, to enhance compression efficiency for the ERP and CMP pictures, respectively. Experimental results demonstrate that both algorithms achieve significant BD-Rate savings and outperform the HM16.17 platform for all-intra (AI), low-delay (LD) and random-access (RA) configurations, respectively. Concretely, compared with the state-of-the-art algorithm WSU-ERP, the proposed EEOA-ERP achieves BD-Rate saving of 0.37% in LD configuration. Furthermore, the proposed EEOA-CMP gains 2.6% on objective quality in RA configuration when compared with the HM16.17 VR CMP under the common test condition.

High Efficiency Video Coding Compliant Perceptual Video Coding Using Entropy Based Visual Saliency Model

In past years, several visual saliency algorithms have been proposed to extract salient regions from multimedia content in view of practical applications. Entropy is one of the important measures to extract salient regions, as these regions have high randomness and attract more visual attention. In the context of perceptual video coding (PVC), computational visual saliency models that utilize the charactertistics of the human visual system to improve the compression ratio are of paramount importance. To date, only a few PVC schemes have been reported that use the visual saliency model. In this paper, we conduct the first attempt to utilize entropy based visual saliency models within the high efficiency video coding (HEVC) framework. The visual saliency map generated for each input video frame is optimally thresholded to generate a binary saliency mask. The proposed HEVC compliant PVC scheme adjusts the quantization parameter according to visual saliency relevance at the coding tree unit (CTU) level. Efficient CTU level rate control is achieved by allocating bits to salient and non-salient CTUs by adjusting the quantization parameter values according to their perceptual weighted map. The attention based on information maximization has shown the best performance on newly created ground truth dataset, which is then incorporated in a HEVC framework. An average bitrate reduction of is achieved by the proposed HEVC compliant PVC scheme with the same perceptual quality and a nominal increase in coding complexity of when compared with HEVC reference software. Moreover, the proposed PVC scheme performs better than other HEVC based PVC schemes when encoded at low data rates.

A Novel Consistent Quality Driven for JEM Based Distributed Video Coding

Distributed video coding (DVC) is an attractive and promising solution for low complexity constrained video applications, such as wireless sensor networks or wireless surveillance systems. In DVC, visual quality consistency is one of the most important issues to evaluate the performance of a DVC codec. However, it is the fact that the quality of the decoded frames that is achieved in most recent DVC codecs is not consistent and it is varied with high quality fluctuation. In this paper, we propose a novel DVC solution named Joint exploration model based DVC (JEM-DVC) to solve the problem, which can provide not only higher performance as compared to the traditional DVC solutions, but also an effective scheme for the quality consistency control. We first employ several advanced techniques that are provided in the Joint exploration model (JEM) of the future video coding standard (FVC) in the proposed JEM-DVC solution to effectively improve the performance of JEM-DVC codec. Subsequently, for consistent quality control, we propose two novel methods, named key frame quantization (KF-Q) and Wyner-Zip frame quantization (WZF-Q), which determine the optimal values of the quantization parameter (QP) and quantization matrix (QM) applied for the key and WZ frame coding, respectively. The optimal values of QP and QM are adaptively controlled and updated for every key and WZ frames to guarantee the consistent video quality for the proposed codec unlike the conventional approaches. Our proposed JEM-DVC is the first DVC codec in literature that employs the JEM coding technique, and then all of the results that are presented in this paper are new. The experimental results show that the proposed JEM-DVC significantly outperforms the relevant DVC benchmarks, notably the DISCOVER DVC and the recent H.265/HEVC based DVC, in terms of both Peak signal-to-noise ratio (PSNR) performance and consistent visual quality.

Improved hierarchical quantisation parameter setting method for screen content coding in high efficiency video coding

Screen content video (SCV) is generated by computers, including texts, animation, and graphics. Unlike conventional camera-captured video, screen contents are often discontinuous, as some regions may remain static for successive frames. Therefore, the traditional hierarchical quantisation parameter (QP) setting scheme for conventional video may be not efficient enough for SCV. In this study, an improved hierarchical QP setting method is proposed for screen content coding. Based on the default hierarchical QP setting in high efficiency video coding (HEVC), the proposed method adjusts QP values at the coding tree unit level according to block matching and content complexity. Smaller QP is assigned to the static region and the translational region to reduce quality refinement, and bigger QP is assigned to the complex and limited quality propagated region to save bits. Experimental results show that compared with the default QP setting method in HEVC, the proposed method reduces up to 15.7% bitrate and 6.3% bitrate on average, and decreases the encoding time by 9.2% averagely.